Models and Languages for Computational Systems Biology (P02013)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-mlcsb

In this course we explore a range of modelling methods for pathways in molecular biology: whether metabolic, signalling, regulatory or transcriptional. These models draw on a rich existing theory of concurrent computational systems, with <em>Petri nets</em> as a unifying basic concept. Techniques range over qualitative and quantitative, discrete and continuous, differential and stochastic models. Working with these models, we look at logics for specifying and characterizing systems' behaviour. Finally, we investigate language-based approaches to modular description and analysis of systems, studying some computationally-inspired biological process calculi.

Entry Requirements

? Pre-requisites : For Informatics PG students and final year MInf students only, or by special permission of the School. Some logic and probability theory. General computer science education.

? Co-requisites : Computational Systems Biology can be taken as a companion course if necessary.

Subject Areas

Home subject area

Computer Science, (School of Informatics, Schedule O)

Other subject areas

Life Science Computing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Not being delivered

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	14:00	14:50	Central
Lecture	Thursday	14:00	14:50	Central

Summary of Intended Learning Outcomes

Describe different ways in which cellular pathways can be modelled, and explain advantages and disadvantages of each.

Model simple pathways using a variety of methods.

Program biological pathways of moderate complexity in a modular way, and employ current tools for their analysis.

Describe process algebra formalisms proposed for modular biological modelling and their comparative advantages and disadvantages.

Read, explore and use the literature on computational modelling in Systems Biology.